1. Field
Apparatuses and methods consistent with exemplary embodiments relate to a method and apparatus for processing a medical image signal, and more particularly, to a method and apparatus capable of estimating brain function activation patterns from brain function data obtained by a medical imaging apparatus.
2. Description of the Related Art
Representative examples of non-invasive measurement techniques include, for example, functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and magnetoencephalography (MEG). Among the techniques, the fMRI has a higher spatial resolution and is widely used. The fMRI is also used to measure various physical quantities, image the measured various physical quantities for detecting an activated region of a brain, and effectively predict brain functions. More specifically, the fMRI captures a cross-section image of a brain that shows a dynamic situation such as, for example, a flow of oxygen in the brain.
When an activity of a particular region of a brain such as neural cells increases so that the region becomes active with increased a metabolic activity, a blood flow supply to a capillary vessel in the region increases, thereby resulting in an increase in a percentage of hemoglobin bound to oxygen within a blood. Hemoglobin bound to oxygen in an activated tissue has a higher signal intensity than hemoglobin which is not been bound to oxygen in surrounding tissues. A signal used to detect such a signal intensity difference is called a blood-oxygenation-level-dependent (BOLD) signal.
The fMRI method measures activation patterns of a desired region of a brain by detecting a BOLD signal and constructing the BOLD signal into a two-dimensional (2D) image. Since it requires about 1 to about 3 seconds to perform a brain scan, a plurality of brain scans may require a period of about 20 to about 30 seconds during which a predetermined task is performed. Examples of the predetermined task may include scanning a participant's brain while under an influence of specific stimuli, thoughts, and exercises.
Thus, the fMRI method continuously measure changes in a magnetic resonance (MR) signal to identify a part of a brain where intensity of the MR signal is increased in response to external stimuli and determine the identified part as an activated region of the brain. That is, the fMRI method measures a degree of functional brain activation by repeatedly measuring the BOLD signal when a brain is activated.
The fMRI uses the same basic principles as MRI in that an anatomical structure of a brain is imaged to reflect a photon density in tissues in vivo, a longitudinal relaxation time, and a transverse relaxation time. However, unlike the MRI, the fMRI additionally measures a local increase in a blood flow in an active region of a brain and a BOLD signal in response thereto. Thus, the fMRI may be used to detect a brain function region (i.e., activated region of a brain) associated with a predetermined task based on a change in a BOLD signal while a participant performs the task. Statistical parametric mapping (SPM) based on a general linear model and an independent component analysis (ICA) may be used to interpret time series data of the BOLD signal measured with the fMRI. In particular, the ICA is a data-driven analysis method that can detect a brain function area without prior information about a task performed by a participant. When applying the ICA method to fMRI data, there is a need for a technique of estimating brain function activation patterns from a BOLD signal with higher accuracy.